Traffic density and traffic flow are important inputs for an intelligent transport system (ITS) to better manage traffic congestion. Presently, these are obtained through loop detectors (LD), traffic radars and surveillance cameras. However, installing loop detectors and traffic radars tends to be difficult and costly. Currently, a more popular way of circumventing this is to develop a Virtual Loop Detector (VLD) by using video content understanding technology to simulate behavior of a loop detector and to further estimate the traffic flow from a surveillance camera. But attempting to obtain a reliable and real-time VLD under changing illumination and weather conditions can be difficult.
Streaming video is defined as continuous transportation of images via Internet and displayed at the receiving end that appears as a video. Video streaming is the process where packets of data in continuous form are provided as input to display devices. Video player takes the responsibility of synchronous processing of video and audio data. The difference between streaming and downloading video is that in downloading video, the video is completely downloaded and no operations can be performed on the file while it is being downloaded. The file is stored in the dedicated portion of a memory. In streaming technology, the video is buffered and stored in a temporary memory, and once the temporary memory is cleared the file is deleted. Operations can be performed on the file even when the file is not completely downloaded.
The main advantage of video streaming is that there is no need to wait for the whole file to be downloaded and processing of the video can start after receiving first packet of data. On the other hand, streaming a high quality video is difficult as the size of high definition video is huge and bandwidth may not be sufficient. Also, the bandwidth has to be good so that the video flow is continuous. It can be safely assumed that for video files of smaller size, downloading technology will provide better results, whereas for larger files the streaming technology is more suitable. Still, there is scope for improvement in streaming technology, by finding an optimized method to stream a high definition video with smaller bandwidth through the selection of key frames for further operations.
Stream mining is a technique to discover useful patterns or patterns of special interest as explicit knowledge from a vast quantity of data. A huge amount of multimedia information including video is becoming prevalent as a result of advances in multimedia computing technologies and high-speed networks. Due to its high information content, extracting video information from continuous data packets is called video stream mining. Video stream mining can be considered subfields of data mining, machine learning and knowledge discovery. In mining applications, the goal of a classifier is to predict the value of the class variable for any new input instance provided with adequate knowledge about class values of previous instances. Thus, in video stream mining, a classifier is trained using the training data (class values of previous instances). The mining process can prove to be ineffective if samples are not a good representation of class value. To get good results from classifier, therefore, the training data should include majority of instance that a class variable can possess.
Heavy traffic congestion of vehicles, mainly during peak hours, creates problems in major cities all around the globe. The ever-increasing amount of small to heavyweight vehicles on the road, poorly designed infrastructure, and ineffective traffic control systems are major causes for traffic congestion. Intelligent transportation system (ITS), with scientific and modern techniques, is a good way to manage the vehicular traffic flows in order to control traffic congestion and for better traffic flow management. To achieve this, ITS takes estimated on-road density as input and analyzes the flow for better traffic congestion management.
One of the most used technologies for determination of traffic density is the Loop Detector (LD) (Stefano et al., 2000). These LDs are placed at the crossings and at different junctures. Once any vehicle passes over, the LD generates signals. Signals from all the LDs placed at crossings are combined and analyzed for traffic density and flow estimation. Recently, a more popular way of circumventing automated traffic analyzer is by using video content understanding technology to estimate the traffic flow from a set of surveillance cameras (Lozano, et. al., 2009; Li, et. al., 2008). Because of low cost and comparatively easier maintenance, video-based systems with multiple CCTV (Closed Circuit Television) cameras are also used in ITS, but mostly for monitoring purpose (Nadeem, et. al., 2004). Multiple screens displaying the video streams from different location are displayed at a central location to observe the traffic status (Jerbi, et. al., 2007; Wen, et. al., 2005; Tiwari, et. al., 2007). Presently, this monitoring system involves the manual task of observing these videos continuously or storing them for lateral use. It will be apparent that in such a set-up, it is very difficult to recognize any real time critical happenings (e.g., heavy congestions).
Recent techniques such as loop detector have major disadvantages of installation and proper maintenance associated with them. Computer vision based traffic application is considered a cost effective option. Applying image analysis and analytics for better congestion control and vehicle flow management in real time has multiple hurdles, and most of them are in research stage. A few of the important limitations for computer vision based technology are as follows:    a. Difficulty in choosing the appropriate sensor for deployment.    b. Trade-off between computational complexity and accuracy.    c. Semantic gap between image content and perception poses challenges to analyze the images, hence it is difficult to decide which feature extraction techniques to use.    d. Finding a reliable and practicable model for estimating density and making global decision.
The major vision based approach for traffic understanding and analyses are object detection and classification, foreground and back ground separation, and local image patch (within ROI) analysis. Detection and classification of moving objects through supervised classifiers (e.g. AdaBoost, Boosted SVM, NN etc.) (Li, et. al., 2008; Ozkurt & Camci, 2009) are efficient only when the object is clearly visible. These methods are quite helpful in counting vehicles and tracking them individually, but in a traffic scenario that involved high overlapping of objects, most of the occluded objects are partially visible and very low object size makes these approaches impracticable. Many researchers tried to separate foreground from background in video sequence either by temporal difference or optical flow (Ozkurt & Camci, 2009). However, such methods are sensitive to illumination change, multiple sources of light reflections and weather conditions. Thus, the vision based approach for automation has its own advantages over other sensors in terms of cost on maintenance and installment process. Still the practical challenges need high quality research to realize it as solution. Occlusion due to heavy traffic, shadows (Janney & Geers, 2009), varied source of lights and sometimes low visibility (Ozkurt & Camci, 2009) makes it very difficult to predict traffic density and flow estimation.
Given low object size, high overlapping between objects and broad field of view in surveillance camera setup, estimation of traffic density by analyzing local patches within the given ROI is an appealing solution. Further, levels of congestion constitute a very important source of information for ITS. This is also used for estimation of average traffic speed and average congestion delay for flow management between stations.
Based on the above mentioned limitations, there is a need for a method and system to estimate vehicular traffic density and apply analytics to monitor and manage traffic flow.